Reliable extrapolation of deep neural operators informed by physics or
sparse observations

Reliable extrapolation of deep neural operators informed by physics or sparse observations

13 December 2022

George Karniadakis

Papers citing "Reliable extrapolation of deep neural operators informed by physics or sparse observations"

10 / 10 papers shown

Title
State-space models are accurate and efficient neural operators for dynamical systems Zheyuan Hu Nazanin Ahmadi Daryakenari Qianli Shen Kenji Kawaguchi George Karniadakis Mamba AI4CE 72 11 0 28 Jan 2025
Neural Parameter Regression for Explicit Representations of PDE Solution Operators Konrad Mundinger Max Zimmer Sebastian Pokutta 50 0 0 19 Mar 2024
Scattering with Neural Operators Sebastian Mizera AI4CE 27 9 0 28 Aug 2023
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 35 4 0 04 Aug 2023
In-Context Operator Learning with Data Prompts for Differential Equation Problems Liu Yang Siting Liu Tingwei Meng Stanley J. Osher 40 59 0 17 Apr 2023
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks K. Michałowska S. Goswami George Karniadakis S. Riemer-Sørensen AI4CE 15 15 0 03 Mar 2023
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils K. Shukla Vivek Oommen Ahmad Peyvan Michael Penwarden L. Bravo A. Ghoshal Robert M. Kirby George Karniadakis 33 19 0 02 Feb 2023
Neural and spectral operator surrogates: unified construction and expression rate bounds L. Herrmann Christoph Schwab Jakob Zech 51 9 0 11 Jul 2022
Generic bounds on the approximation error for physics-informed (and) operator learning Tim De Ryck Siddhartha Mishra PINN 59 59 0 23 May 2022
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 226 2,287 0 18 Oct 2020